Quality of Information in Wireless Sensor Networks: A Survey 1 (Completed paper)

Abstract: In Wireless Sensor Networks (WSNs) the operating conditions and/or user requirements are often desired to be evolvable, whether driven by changes of the monitored parameters or WSN properties of configuration, structure, communication capacities, node density, and energy among many others. While considering evolvability, delivering the required information with the specified quality (accuracy, timeliness, reliability etc) defined by the user constitutes a key objective of WSNs. Most existing research efforts handle fluctuations of operation conditions in order to deliver information with the highest possible specified quality. In this paper, we take these aspects into consideration and survey existing work on Quality of Information (QoI). As a contribution, we categorize WSN information into a set of abstract classes for generality across varied application types. Our survey shows that currently QoI is usually addressed in isolation by focusing on discrete data processing operations/building blocks such as raw data collection, in-network processing (compression, aggregation), information transport and sink operations for decision making. This survey comprehensively explains the different views of QoI on attributes, metrics and WSN functional operations mapped with existing approaches. The survey also forms the basis for specifying needed QoI research issues

A two-queue model for optimising the value of information in energy-harvesting sensor networks

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Monitoring the Quality of Information (QoI) for low-cost sensor networks

Mantaining a low cost sensor network calibrated for a certain time having trustable information is a highly complicated task. Thanks to redundant sensors in sensing devices and a sensor network, statistical tests can be applied in order to know whenever a calibration or a replacement should be done

Optimal data collection in wireless sensor networks with correlated energy harvesting

We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision-making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate

A Survey of Enabling Technologies for Smart Communities

In 2016, the Japanese Government publicized an initiative and a call to action for the implementation of a Super Smart Society announced as Society 5.0. The stated goal of Society 5.0 is to meet the various needs of the members of society through the provisioning of goods and services to those who require them, when they are required and in the amount required, thus enabling the citizens to live an active and comfortable life. In spite of its genuine appeal, details of a feasible path to Society 5.0 are conspicuously missing. The first main goal of this survey is to suggest such an implementation path. Specifically, we define a Smart Community as a human-centric entity where technology is used to equip the citizenry with information and services that they can use to inform their decisions. The arbiter of this ecosystem of services is a Marketplace of Services that will reward services aligned with the wants and needs of the citizens, while discouraging the proliferation of those that are not. In the limit, the Smart Community we defined will morph into Society 5.0. At that point, the Marketplace of Services will become a platform for the co-creation of services by a close cooperation between the citizens and their government. The second objective and contribution of this survey paper is to review known technologies that, in our opinion, will play a significant role in the transition to Society 5.0. These technologies will be surveyed in chronological order, as newer technologies often extend old technologies while avoiding their limitations

Formal analysis of a calculus for WSNs from quality perspective

In viewing the common unreliability problem in wireless communications, the CWQ calculus (a Calculus for Wireless sensor networks from Quality perspective) was recently proposed for modeling and reasoning about WSNs (Wireless Sensor Networks) and their applications from a quality perspective. The CWQ calculus ensures that sensor nodes, even though in an unreliable communication network, can still behave in a reasonable manner using default values. Nevertheless, the topological structure in CWQ calculus is considered at the network level and it is tightly coupled with the processes and other configurations; this may limit its flexibility. In this paper, we extend our previous CWQ calculus to be a parametric framework to make it more flexible to be able to model and reason about networks of different topological structures. In the parametric framework, we extract the topological structure of a network and make it to be a configuration so that all topological structure changes can be captured by this framework

Towards Aggregating Time-Discounted Information in Sensor Networks

Sensor networks are deployed to monitor a seemingly endless list of events in a multitude of application domains. Through data collection and aggregation enhanced with data mining and machine learning techniques, many static and dynamic patterns can be found by sensor networks. The aggregation problem is complicated by the fact that the perceived value of the data collected by the sensors is affected by many factors such as time, location and user valuation. In addition, the value of information deteriorates often dramatically over time. Through our research, we already achieved some results: A formal algebraic analysis of information discounting, especially affected by time. A general model and two specific models are developed for information discounting. The two specific models formalize exponetial time-discount and linear time-discount. An algebraic analysis of aggregation of values that decay with time exponentially. Three types of aggregators that offset discounting effects are formalized and analyzed. A natural synthesis of these three aggregators is discovered and modeled. We apply our theoretical models to emergency response with thresholding and confirm with extensive simulation. For long-term monitoring tasks, we laid out a theoretical foundation for discovering an emergency through generations of sensors, analysed the achievability of a long-term task and found an optimum way to distribute sensors in a monitored area to maximize the achievability. We proposed an implementation for our alert system with state-of-art wireless microcontrollers, sensors, real-time operating systems and embedded internet protocols. By allowing aggregation of time-discounted information to proceed in an arbitrary, not necessarily pairwise manner, our results are also applicable to other similar homeland security and military application domains where there is a strong need to model not only timely aggregation of data collected by individual sensors, but also the dynamics of this aggregation. Our research can be applied to many real-world scenarios. A typical scenario is monitoring wildfire in the forest: A batch of first-generation sensors are deployed by UAVs to monitor a forest for possible wildfire. They monitor various weather quantities and recognize the area with the highest possibility of producing a fire --- the so-called area of interest (AoI). Since the environment changes dynamically, so after a certain time, the sensors re-identify the AoI. The value of the knowledge they learned about the previous AoI decays with time quickly, our methods of aggregation of time-discounted information can be applied to get update knowledge. Close to depletion of their energy of the current generation of sensors, a new generation of sensors are deployed and inherit the knowledge from the current generation. Through this way, monitoring long-term tasks becomes feasible. At the end of this thesis, we propose some extensions and directions from our current research: Generalize and extend the special classes of Type 1 and Type 2 aggregation operators; Analyze aggregation operator of Type 3 and Type 4, find some special applicable candidates; Data aggregation across consecutive generations of sensors in order to learn about events with discounting that take a long time to manifest themselves; Network implications of various aggregation strategies; Algorithms for implementation of some special classes of aggregators. Implement wireless sensor network that can autonomously learn and recognize patterns of emergencies, predict incidents and trigger alarms through machine learning

QOS-Aware and Status-Aware Adaptive Resource Allocation Framework in SDN-Based IOT Middleware

«L’Internet des objets (IdO) est une infrastructure mondiale pour la société de l’information, qui permet de disposer de services évolués en interconnectant des objets (physiques ou virtuels) grâce aux technologies de l’information et de la communication interopérables existantes ou en évolution. »[1] La vision de l’Internet des Objets est d’étendre l’Internet dans nos vies quotidiennes afin d’améliorer la qualité de vie des personnes, de sorte que le nombre d’appareils connectés et d’applications innovantes augmente très rapidement pour amener l’intelligence dans différents secteurs comme la ville, le transport ou la santé. En 2020, les études affirment que les appareils connectés à Internet devraient compter entre 26 milliards et 50 milliards d’unités. [2, 3] La qualité de service d’application IoT dépend non seulement du réseau Internet et de l’infrastructure de communication, mais aussi du fonctionnement et des performances des appareils IoT. Par conséquent, les nouveaux paramètres de QoS tels que la précision des données et la disponibilité des appareils deviennent importants pour les applications IoT par rapport aux applications Internet. Le grand nombre de dispositifs et d’applications IoT connectés à Internet, et le flux de trafic spontané entre eux rendent la gestion de la qualité de service complexe à travers l’infrastructure Internet. D’un autre côté, les dispositifs non-IP et leurs capacités limitées en termes d’énergie et de transmission créent l’environnement dynamique et contraint. De plus, l’interconnexion de bout en bout entre les dispositifs et les applications n’est pas possible. Aussi, les applications sont intéressées par les données collectées, pas à la source spécifique qui les produit. Le Software Defined Networking (SDN) est un nouveau paradigme pour les réseaux informatiques apparu récemment pour cacher la complexité de l’architecture de réseau traditionnelle (par exemple de l’Internet) et briser la fermeture des systèmes de réseau dans les fonctions de contrôle et de données. Il permet aux propriétaires et aux administrateurs de réseau de contrôler et de gérer le comportement du réseau par programme, en découplant le plan de contrôle du plan de données. SDN a le potentiel de révolutionner les réseaux informatiques classiques existants, en offrant plusieurs avantages tels que la gestion centralisée, la programmabilité du réseau, l’efficacité des coûts d’exploitation, et les innovations. Dans cette thèse, nous étudions la gestion de ressources sur l’infrastructure IoT, y compris les réseaux de transport/Internet et de détection. Nous profitons de la technologie SDN comme le futur d’Internet pour offrir un système de support QoS flexible et adaptatif pour les services IoT. Nous présentons un intergiciel basé sur SDN pour définir un cadre de gestion de QoS pour gérer les besoins spécifiques de chaque application à travers l’infrastructure IoT. De plus, nous proposons un nouveau modèle QoS qui prend en compte les préférences de QoS des applications et l’état des éléments de réseau pour allouer efficacement les ressources sur le réseau transport/Internet basé sur SDN tout en maximisant les performances du réseau.----------ABSTRACT: The Internet of Things (IoT) is an integration of various kinds of technologies, wherein heterogeneous objects with capabilities of sensing, actuation, communication, computation, networking, and storage are rapidly developed to collect the data for the users and applications. The IoT vision is to extend the Internet into our everyday lives, so the number of connected devices and innovative applications are growing very fast to bring intelligence into as many domains as possible. The QoS for IoT application not only depends on the Internet network and communication infrastructure, it is also impacted by the operation and performance of IoT sensing infrastructure. Therefore, the new QoS parameters such as data accuracy, sampling rate, and device availability become important for the IoT applications compared to the Internet applications. The huge number of the Internet-connected IoT devices and application, and the spontaneous traffic flow among them make the management of the quality of service complex across the Internet infrastructure. On the other hand, the non-IP devices and their limited capabilities in terms of energy and transmission create the dynamic environment and hinder the direct interaction between devices and applications. The quality of service is becoming one of the critical non-functional IoT element which needs research and studies. A flexible and scalable QoS management mechanism must be implemented in IoT system to keep up with the growth rate of the Internet-connected IoT devices and applications as well as their heterogeneity and diversity. The solution should address the IoT application requirements and user satisfaction while considering the system dynamism, limitations, and characteristics. Software-Defined Networking (SDN) is an emerging paradigm in computer networking which separates the control plane and the data plane of the network elements. It makes the network elements programmable via the centralized control plane. This approach enables more agile management and control over the network behavior. In this thesis, we take advantage of SDN technology as the future of the Internet to offer a flexible and adaptive QoS support scheme for the IoT services. We present an SDN-based middleware to define a QoS management framework to manage the application specific QoS needs across the IoT infrastructure including transport and sensing network. Also, we propose a new QoS model that takes into account the application QoS preferences and the network elements status to allocate effectively the resources for the applications across SDN network while maximizing network performance

Cooperative mobility maintenance techniques for information extraction from mobile wireless sensor networks

Recent advances in the development of microprocessors, microsensors, ad-hoc wireless networking and information fusion algorithms led to increasingly capable Wireless Sensor Networks (WSNs). Besides severe resource constraints, sensor nodes mobility is considered a fundamental characteristic of WSNs. Information Extraction (IE) is a key research area within WSNs that has been characterised in a variety of ways, ranging from a description of its purposes to reasonably abstract models of its processes and components. The problem of IE is a challenging task in mobile WSNs for several reasons including: the topology changes rapidly; calculation of trajectories and velocities is not a trivial task; increased data loss and data delivery delays; and other context and application specific challenges. These challenges offer fundamentally new research problems. There is a wide body of literature about IE from static WSNs. These approaches are proved to be effective and efficient. However, there are few attempts to address the problem of IE from mobile WSNs. These attempts dealt with mobility as the need arises and do not deal with the fundamental challenges and variations introduced by mobility on the WSNs. The aim of this thesis is to develop a solution for IE from mobile WSNs. This aim is achieved through the development of a middle-layer solution, which enables IE approaches that were designed for the static WSNs to operate in the presence of multiple mobile nodes. This thesis contributes toward the design of a new self-stabilisation algorithm that provides autonomous adaptability against nodes mobility in a transparent manner to both upper network layers and user applications. In addition, this thesis proposes a dynamic network partitioning protocol to achieve high quality of information, scalability and load balancing. The proposed solution is flexible, may be applied to different application domains, and less complex than many existing approaches. The simplicity of the solutions neither demands great computational efforts nor large amounts of energy conservation. Intensive simulation experiments with real-life parameters provide evidence of the efficiency of the proposed solution. Performance experimentations demonstrate that the integrated DNP/SS protocol outperforms its rival in the literature in terms of timeliness (by up to 22%), packet delivery ratio (by up to 13%), network scalability (by up to 25%), network lifetime (by up to 40.6%), and energy consumption (by up to 39.5%). Furthermore, it proves that DNP/SS successfully allows the deployment of static-oriented IE approaches in hybrid networks without any modifications or adaptations